Just like in the stock market [1]}, [2]}, [3]}, deep learning models have become popular tools for price prediction in cryptocurrency markets [4]}, [5]}, [6]}, [7]}, [8]}, [9]}. Looking at time series in general, recurrent neural networks, such as long-short term memory models (LSTMs) [10]} and gated recurrent unit (GRUs) [11]} have been widely used for forecasting.
When it comes to volatility prediction, [12]} proposed an architecture based on convolutional neural networks (CNNs) and long-short term memory (LSTM) units to forecast gold volatility. LSTMs were also used by [13]} to forecast currency exchange rate volatility. Finally, temporal convolutional neural networks have been used with Twitter sentiment data to predict Bitcoin volatility [14]}.
